A Novel Wavelet-Based Ensemble Method for Short-Term Load Forecasting with Hybrid Neural Networks and Feature Selection

• Published in: IEEE transactions on power systems Vol. 31; no. 3; pp. 1788 - 1798
• Main Authors: Song Li, Peng Wang, Goel, Lalit
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Ensemble method; extreme learning machine; Forecasting; Forecasting techniques; load forecast; Load forecasting; Neural networks; partial least-squares regression; Training; wavelet transform; Wavelet transforms
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/TPWRS.2015.2438322

In this paper, a new ensemble forecasting model for short-term load forecasting (STLF) is proposed based on extreme learning machine (ELM). Four important improvements are used to support the ELM for increased forecasting performance. First, a novel wavelet-based ensemble scheme is carried out to generate the individual ELM-based forecasters. Second, a hybrid learning algorithm blending ELM and the Levenberg-Marquardt method is proposed to improve the learning accuracy of neural networks. Third, a feature selection method based on the conditional mutual information is developed to select a compact set of input variables for the forecasting model. Fourth, to realize an accurate ensemble forecast, partial least squares regression is utilized as a combining approach to aggregate the individual forecasts. Numerical testing shows that proposed method can obtain better forecasting results in comparison with other standard and state-of-the-art methods.